Robust control of mobile robots needs to take into account the many noise sources in the sensors and actuators of the robots. In this paper, we propose the application of control strategies designed for deterministic chaotic systems to the design of robust control of mobile robots. The deterministic chaotic system hypothesis is proposed as an intermediate model between the noise free system and the pure random perturbations.

In this paper we give a formal definition of the high-order Boltzmann machine (BM), and extend the well-known results on the convergence of the learning algorithm of the two-order BM. From the Bahadur-Lazarsfeld expansion we characterize the probability distribution learned by the high order BM. Likewise a criterion is given to establish the topology of the BM depending on the significant correlations of the particular probability distribution to be learned.

Boltzmann machines (BMs) are proposed as a computational model for the solution of the satisfiability (SAT) problem in the propositional calculus setting. Conditions that guarantee consensus function maxima for configurations of the BM associated with solutions to the satisfaction problem are given. Experimental results that show a linear behavior of BMs solving the satisfiability problem are presented and discussed.>